TO CHANGE OR NOT TO CHANGE: HOW MOTOR CARRIERS RESPONDED FOLLOWING 9/11
Bibliographic record
Abstract
What happens when firms are confronted by a strategic surprise—defined as “sudden, urgent, unfamiliar change” (Ansoff 1975, p. 22)—such as the terrorist attacks that occurred on September 11, 2001? Numerous studies have examined how strategic change, in the aftermath of a significant environmental event, contributes to organizational survival and success. But, is strategic change the appropriate response to unexpected and disruptive environmental change? And is there a preferred trajectory for change, such that certain strategies are better suited than others to the post‐surprise environment? This exploratory research examines whether or not strategic change is an appropriate response to strategic surprise affecting the firms in the trucking industry by considering the actions of motor carriers in the aftermath of 9/11. The data evidences significant disruption to the trucking industry following the event: among the sample, mean operating ratios declined by more than 50%. While nearly 40% of the carriers studied changed strategies in the post‐9/11 environment, this did not guarantee better performance. All carriers fared worse following the attacks, but those carriers that changed strategies actually performed significantly worse than those that persisted with pre‐9/11 strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".